Semi Conditional Planners For Efficient Planning Under Uncertainty With Macro Actions
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Semi Conditional Planners For Efficient Planning Under Uncertainty With Macro Actions
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Author : Ruijie He
language : en
Publisher:
Release Date : 2010
Semi Conditional Planners For Efficient Planning Under Uncertainty With Macro Actions written by Ruijie He and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010 with categories.
Planning in large, partially observable domains is challenging, especially when good performance requires considering situations far in the future. Existing planners typically construct a policy by performing fully conditional planning, where each future action is conditioned on a set of possible observations that could be obtained at every timestep. Unfortunately, fully-conditional planning can be computationally expensive, and state-of-the-art solvers are either limited in the size of problems that can be solved, or can only plan out to a limited horizon. We propose that for a large class of real-world, planning under uncertainty problems, it is necessary to perform far-lookahead decision-making, but unnecessary to construct policies that condition all actions on observations obtained at the previous timestep. Instead, these problems can be solved by performing semi conditional planning, where the constructed policy only conditions actions on observations at certain key points. Between these key points, the policy assumes that a macro-action - a temporally-extended, fixed length, open-loop action sequence, comprising a series of primitive actions, is executed. These macro-actions are evaluated within a forward-search framework, which only considers beliefs that are reachable from the agent's current belief under different actions and observations; a belief summarizes an agent's past history of actions and observations. Together, semi-conditional planning in a forward search manner restricts the policy space in exchange for conditional planning out to a longer-horizon. Two technical challenges have to be overcome in order to perform semi-conditional planning efficiently - how the macro-actions can be automatically generated, as well as how to efficiently incorporate the macro action into the forward search framework. We propose an algorithm which automatically constructs the macro-actions that are evaluated within a forward search planning framework, iteratively refining the macro actions as more computation time is made available for planning. In addition, we show that for a subset of problem domains, it is possible to analytically compute the distribution over posterior beliefs that result from a single macro-action. This ability to directly compute a distribution over posterior beliefs enables us to enjoy computational savings when performing macro-action forward search. Performance and computational analysis for the algorithms proposed in this thesis are presented, as well as simulation experiments that demonstrate superior performance relative to existing state-of-the-art solvers on large planning under uncertainty domains. We also demonstrate our planning under uncertainty algorithms on target-tracking applications for an actual autonomous helicopter, highlighting the practical potential for planning in real-world, long-horizon, partially observable domains.
Automated Planning
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Author : Malik Ghallab
language : en
Publisher: Elsevier
Release Date : 2004-05-03
Automated Planning written by Malik Ghallab and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004-05-03 with Business & Economics categories.
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Planning Manual
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Author : Charles E. Yoe
language : en
Publisher:
Release Date : 1996
Planning Manual written by Charles E. Yoe and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1996 with Planning categories.
Probabilistic Robotics
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Author : Sebastian Thrun
language : en
Publisher: MIT Press
Release Date : 2005-08-19
Probabilistic Robotics written by Sebastian Thrun and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005-08-19 with Technology & Engineering categories.
An introduction to the techniques and algorithms of the newest field in robotics. Probabilistic robotics is a new and growing area in robotics, concerned with perception and control in the face of uncertainty. Building on the field of mathematical statistics, probabilistic robotics endows robots with a new level of robustness in real-world situations. This book introduces the reader to a wealth of techniques and algorithms in the field. All algorithms are based on a single overarching mathematical foundation. Each chapter provides example implementations in pseudo code, detailed mathematical derivations, discussions from a practitioner's perspective, and extensive lists of exercises and class projects. The book's Web site, www.probabilistic-robotics.org, has additional material. The book is relevant for anyone involved in robotic software development and scientific research. It will also be of interest to applied statisticians and engineers dealing with real-world sensor data.
Robotic Mapping And Exploration
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Author : Cyrill Stachniss
language : en
Publisher: Springer
Release Date : 2009-05-06
Robotic Mapping And Exploration written by Cyrill Stachniss and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-05-06 with Technology & Engineering categories.
"Robotic Mapping and Exploration" is an important contribution in the area of simultaneous localization and mapping (SLAM) for autonomous robots, which has been receiving a great deal of attention by the research community in the latest few years. The contents are focused on the autonomous mapping learning problem. Solutions include uncertainty-driven exploration, active loop closing, coordination of multiple robots, learning and incorporating background knowledge, and dealing with dynamic environments. Results are accompanied by a rich set of experiments, revealing a promising outlook toward the application to a wide range of mobile robots and field settings, such as search and rescue, transportation tasks, or automated vacuum cleaning.
Decision Making Under Uncertainty
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Author : Mykel J. Kochenderfer
language : en
Publisher: MIT Press
Release Date : 2015-07-24
Decision Making Under Uncertainty written by Mykel J. Kochenderfer and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-07-24 with Computers categories.
An introduction to decision making under uncertainty from a computational perspective, covering both theory and applications ranging from speech recognition to airborne collision avoidance. Many important problems involve decision making under uncertainty—that is, choosing actions based on often imperfect observations, with unknown outcomes. Designers of automated decision support systems must take into account the various sources of uncertainty while balancing the multiple objectives of the system. This book provides an introduction to the challenges of decision making under uncertainty from a computational perspective. It presents both the theory behind decision making models and algorithms and a collection of example applications that range from speech recognition to aircraft collision avoidance. Focusing on two methods for designing decision agents, planning and reinforcement learning, the book covers probabilistic models, introducing Bayesian networks as a graphical model that captures probabilistic relationships between variables; utility theory as a framework for understanding optimal decision making under uncertainty; Markov decision processes as a method for modeling sequential problems; model uncertainty; state uncertainty; and cooperative decision making involving multiple interacting agents. A series of applications shows how the theoretical concepts can be applied to systems for attribute-based person search, speech applications, collision avoidance, and unmanned aircraft persistent surveillance. Decision Making Under Uncertainty unifies research from different communities using consistent notation, and is accessible to students and researchers across engineering disciplines who have some prior exposure to probability theory and calculus. It can be used as a text for advanced undergraduate and graduate students in fields including computer science, aerospace and electrical engineering, and management science. It will also be a valuable professional reference for researchers in a variety of disciplines.
An Introduction To The Planning Domain Definition Language
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Author : Patrik Kulkarni
language : en
Publisher: Springer Nature
Release Date : 2022-05-31
An Introduction To The Planning Domain Definition Language written by Patrik Kulkarni and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-31 with Computers categories.
Planning is the branch of Artificial Intelligence (AI) that seeks to automate reasoning about plans, most importantly the reasoning that goes into formulating a plan to achieve a given goal in a given situation. AI planning is model-based: a planning system takes as input a description (or model) of the initial situation, the actions available to change it, and the goal condition to output a plan composed of those actions that will accomplish the goal when executed from the initial situation. The Planning Domain Definition Language (PDDL) is a formal knowledge representation language designed to express planning models. Developed by the planning research community as a means of facilitating systems comparison, it has become a de-facto standard input language of many planning systems, although it is not the only modelling language for planning. Several variants of PDDL have emerged that capture planning problems of different natures and complexities, with a focus on deterministic problems. The purpose of this book is two-fold. First, we present a unified and current account of PDDL, covering the subsets of PDDL that express discrete, numeric, temporal, and hybrid planning. Second, we want to introduce readers to the art of modelling planning problems in this language, through educational examples that demonstrate how PDDL is used to model realistic planning problems. The book is intended for advanced students and researchers in AI who want to dive into the mechanics of AI planning, as well as those who want to be able to use AI planning systems without an in-depth explanation of the algorithms and implementation techniques they use.
Basic Methods Of Policy Analysis And Planning
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Author : Carl Patton
language : en
Publisher: Routledge
Release Date : 2015-08-26
Basic Methods Of Policy Analysis And Planning written by Carl Patton and has been published by Routledge this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-08-26 with Political Science categories.
Updated in its 3rd edition, Basic Methods of Policy Analysis and Planning presents quickly applied methods for analyzing and resolving planning and policy issues at state, regional, and urban levels. Divided into two parts, Methods which presents quick methods in nine chapters and is organized around the steps in the policy analysis process, and Cases which presents seven policy cases, ranging in degree of complexity, the text provides readers with the resources they need for effective policy planning and analysis. Quantitative and qualitative methods are systematically combined to address policy dilemmas and urban planning problems. Readers and analysts utilizing this text gain comprehensive skills and background needed to impact public policy.
Automated Planning And Acting
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Author : Malik Ghallab
language : en
Publisher: Cambridge University Press
Release Date : 2016-08-09
Automated Planning And Acting written by Malik Ghallab and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-08-09 with Computers categories.
This book presents the most recent and advanced techniques for creating autonomous AI systems capable of planning and acting effectively.
Heuristic Search
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Author : Stefan Edelkamp
language : en
Publisher: Elsevier
Release Date : 2011-05-31
Heuristic Search written by Stefan Edelkamp and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-05-31 with Computers categories.
Search has been vital to artificial intelligence from the very beginning as a core technique in problem solving. The authors present a thorough overview of heuristic search with a balance of discussion between theoretical analysis and efficient implementation and application to real-world problems. Current developments in search such as pattern databases and search with efficient use of external memory and parallel processing units on main boards and graphics cards are detailed. Heuristic search as a problem solving tool is demonstrated in applications for puzzle solving, game playing, constraint satisfaction and machine learning. While no previous familiarity with heuristic search is necessary the reader should have a basic knowledge of algorithms, data structures, and calculus. Real-world case studies and chapter ending exercises help to create a full and realized picture of how search fits into the world of artificial intelligence and the one around us. Provides real-world success stories and case studies for heuristic search algorithms Includes many AI developments not yet covered in textbooks such as pattern databases, symbolic search, and parallel processing units